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检索条件"任意字段=4th International Conference on Computing and Data Science, CONF-CDS 2022"

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Two Applications of Manifold Regularization in Deep Learning Architectures 4

Two Applications of Manifold Regularization in Deep Learning...

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4th international conference on computing and data science, conf-cds 2022

作者： Yuan, Weixuan Zhang, Chengyuan Song, Wenrui Yang, Siyi Technical University of Munich Munich80333 Germany School of Science Harbin Institute of Technology Shenzhen Shenzhen518055 China Rizhao Middle School Rizhao276899 China Shanghai Pinghe School Shanghai201206 China

With the rapid development of machine learning today, deep learning has shown great advantages in many fields. Due to the high cost of datasets with labels, its branch semi-supervised learning has become an important research area. However, existing semi-supervised learning methods perform poorly on extremely small datasets, for instance, only one labeled example for each class. Hence, we introduce methods that apply manifold approaches to DL(Deep Learning) and frameworks that implement the idea. Potential application scenarios were analyzed based on experiments on synthetic and real-world experiments. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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data Generation Using Simulation Technology to Improve Perception Mechanism of Autonomous Vehicles 4

Data Generation Using Simulation Technology to Improve Perce...

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4th international conference on computing and data science, conf-cds 2022

作者： Cao, Minh Ramezani, Ramin Bioengineering Dept University of California Los Angeles Los AngelesCA90095 United States Computer Science Dept University of California Los Angeles Los AngelesCA90095 United States

Recent advancements in computer graphics technology allow more realistic rendering of car driving environments. they have enabled self-driving car simulators such as DeepGTA-V and CARLA (Car Learning to Act) to generate large amounts of synthetic data that can complement the existing real-world dataset in training autonomous car perception. Furthermore, since self-driving car simulators allow full control of the environments, they can generate dangerous driving scenarios that the real-world dataset lacks such as bad weather and accident scenarios. In this paper, we will demonstrate the effectiveness of combining data gathered from the real-world with data generated in simulated world to train perception system on object detection and localization task. We will also propose a multi-level deep learning perception framework that aims to emulate a human learning experience in which series of tasks from simple to more difficult ones are learnt in a certain domain. the autonomous car perceptron can learn from easy-to-drive scenarios to more challenging ones customized by simulation software. © Published under licence by IOP Publishing Ltd.

关键词： Computer vision

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Research on Cloud-Fog computing Task Scheduling Based on Classification Mining 4

Research on Cloud-Fog Computing Task Scheduling Based on Cla...

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4th IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Liu, Lindong Zeng, Xiaoning Qin, Yuehai Guangdong University of Education School of Computer Science Guangzhou China Guangdong University of Education School of Mathematics Guangzhou China

ISBN: (纸本)9798350363586

In the Fog computing environment, it is necessary to study the task scheduling problem of Cloud-fog in order to meet the resource requirements of task scheduling, reduce data transmission during task scheduling, and improve the throughput of Fog computing systems. By improving and optimizing the classification mining algorithm, the classification mining algorithm is integrated into the Cloud and Fog task scheduling model, integrating the characteristics of Fog and Cloud. A Cloud-Fog fusion task scheduling model based on classification mining and CFCM task scheduling algorithm are proposed in the paper. the algorithm first schedules the corresponding tasks directly to the Fog layer or Cloud layer based on the generated classification rules, and then transfers the tasks to specific computing nodes of Cloud layers or Fog layer. Simulation experiments showed that the CFCM algorithm has better performance in reducing task execution time and average waiting time compared to similar algorithms. © 2024 IEEE.

关键词： Scheduling algorithms

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PancakeFS: A Write Efficiently and Read Optimized Filesystem 4

PancakeFS: A Write Efficiently and Read Optimized Filesystem

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4th international conference on Consumer Electronics and Computer Engineering, ICCECE 2024

作者： Wang, Yongkang Liu, Yang Chen, Zhiguang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Institute for Quantum Information College of Computer National University of Defense Technology State Key Laboratory of High-Performance Computing Changsha China

ISBN: (纸本)9798350384062

With the development of emerging technologies such as cloud computing and large AI models (such as LLM), many applications have placed higher demands on the intensive read and write processing of massive data. Addressing the limitations of traditional file systems, we propose PancakeFS, an LSM-Tree-based file system. Leveraging the write-friendly characteristics of LSM-Tree, PancakeFS exhibits efficient write performance. We improve read performance through the design of an inode allocation algorithm based on subtree partitioning and optimization of the disk layout for hot data reconstruction. Our experimental results demonstrate that PancakeFS outperforms Ext4 and TableFS in terms of both read and write performance. © 2024 IEEE.

关键词： data handling

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An Automatic Wound Detection System Empowered by Deep Learning 4

An Automatic Wound Detection System Empowered by Deep Learni...

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4th international conference on computing and data science, conf-cds 2022

作者： Adnan, Muhammad Asif, Muhammad Ahmad, Maaz Bin Mahmood, Toqeer Masood, Khalid Ashraf, Rehan Nadeem Faisal, C.M. Department of Computer Science Lahore Garrison University Lahore Pakistan College of Computing and Information Science KIET Karachi Pakistan Department of Computer Science National Textile University Faisalabad Pakistan

the wound can be defined as a breakdown in the protective function of the skin. Perceiving the types of wounds at the initial stages can be more viable in the treatment process. the paramedical staff usually examines the wound with its size and healing status is determined through visual assessment. Telemedicine and rest of the healthcare services delivered through digital means aim to ensure the availability of health facilities at doorstep. Especially, in COVID-19 period these services played significant role where physical examination of patient in hospital may enhance the spread of the disease. the remote assessment of wound demands high quality of medical images or videos and localization of wound area. the manual diagnosis is not very robust and reliable because of variability in the appearance of wound site. Whereas an automatic wound detection, localization and classification may assist a physician with more accuracy and robustness. In this work, an automatic wound detection technique using YOLO v3 model is proposed. the proposed technique detects, localizes and classifies the wound into four main categories containing stitch-wound, cut-wound, open-wound and normal-skin. the experimental results show that the proposed technique is more efficient and robust with 99% accuracy and outperforms other counter parts. © Published under licence by IOP Publishing Ltd.

关键词： Telemedicine

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Detection of Ischemic Brain Stroke using Deep Learning 4

Detection of Ischemic Brain Stroke using Deep Learning

引用

4th international conference on computing and data science, conf-cds 2022

作者： Sharmila C Santhiya S Poongundran M Sanjeeth S Pranesh S Computer Science and Engineering Kongu Engineering College

Stroke is a severe disease that is currently the country's fourth greatest reason of *** stroke is a medical emergency that necessitates prompt triage,diagnosis,and treatment.A patient's stroke lesion site must be identified as soon as feasible and properly to assess the risks and give more early and efficient *** is essential to develop automated methods for identifying and segmenting stroke *** this work,MRI is sliced into twodimensional network groups and used each division set to train sessions and predict ***,before slicing and splitting to produce a 3 D lesion result,twodimensional slices results of all groups are arranged according to the position order in the MRI.

关键词： CNN Deep learning Image Segmentation Ischaemic Stroke UNet

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A Survey on Privacy-Preserving Communication Frameworks in Machine Learning for Cybersecurity threat Detection 4

A Survey on Privacy-Preserving Communication Frameworks in M...

引用

4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Suneetha, Banothu Kesavan, R. Saveetha School of Engineering Simats Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331529635

this survey covers the new trend of privacy-preserving communication frameworks in machine learning in the FL, DP and SMPC for cyber threat detection domains. the specific research performed between the year of 2020 and 2024 has been surveyed and included in this paper. the main contribution is monitoring cybersecurity threats using privacy-preserving frameworks in distributed systems such as IoT and industrial networks. 40 research papers have been reviewed and categorized to several related themes such as FL implementations, privacy-preserving methods, and cybersecurity threat detection. the relevance of privacy-preserving frameworks for the real-world cybersecurity scenario is illustrated with dataset CISA 2022 Vulnerability dataset. the study of privacy-preserving communication shows that FL can be combined with DP and SMPC to achieve data privacy preservation while deduction accuracy of detecting cyber threats can be well-maintained. Moreover, the research gaps and challenges of scalability, communication overhead have been addressed and identified to facilitate the future research and development. © 2024 IEEE.

关键词： Federated learning

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Resource-Efficient Hybrid CNN-HMM Approach for Heart Rate Classification 4

Resource-Efficient Hybrid CNN-HMM Approach for Heart Rate Cl...

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4th international conference on Artificial Intelligence and Signal Processing

作者： Avinash, Baruri Sai Senthil, Varun Kumar, Binod IIT Jodhpur Dept EE Jodhpur Rajasthan India

ISBN: (纸本)9798350350661;9798350350654

there is a growing interest in using Deep learning (DL) to analyse sensor data for monitoring heart-rate, aiming for earlier and accurate prediction of cardiovascular diseases (CvD). However, the resource-intensive nature of DL pose challenges, particularly on memory-limited devices like smartphones and other edge devices. To address this issue, we propose a novel approach that combines a Convolutional Neural Network-Hidden Markov Model (CNN-HMM). In this model, features from signals on smartphones or other edge devices are extracted using a CNN block, while classification is performed using an HMM. this CNN-HMM model offers advantages in processing non-stationary data and effectively extracting heart rate information. Additionally, we aim to address the challenge of Deep Neural Network (DNN) inference on resource-constrained edge devices by systematically comparing different light-weight models on several commercial hardware platforms (Raspberry Pi 4, high-performance computing (HPC) platforms) across popular frameworks, highlighting their performance and limitations. the proposed approach has achieved an accuracy of 96.39%, a precision of 96.27%, a recall of 97.20%, and an F1-score of 96.73% on NVIDIA Tesla T4 GPU (Graphics Processing Unit). Additionally, our model has demonstrated enhanced throughput, outperforming fine-tuned MobileNetV2, NASNet-Mobile, and MobileNetV3Small models by 77.62%, 138.65%, and 39.92% respectively on the Raspberry Pi 4 hardware platform.

关键词： Heart rate classification Cardiovascular diseases Deep Neural Network Convolutional Neural Network-Hidden Markov Model High performance computing Deep Learning Graphics Processing Unit

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Mining of Microsatellites in Genomic data Using a Hybrid Approach with Range Tree Applications 7

Mining of Microsatellites in Genomic Data Using a Hybrid App...

引用

7th international conference on computing, Communication and Security, ICCCS 2022 and 2022 4th international conference on Big data and Computational Intelligence, ICBDCI 2022

作者： Umang Bharti, P.K. Husain, Akhtar Shri Venkateshwara University School of Computer Science UP Gajraula India MJP Rohilkhand University Department of CS and IT U P Bareilly India

ISBN: (纸本)9798350309904

Microsatellites are molecular markers with ubiquitous repeat patterns found in genome sequences. they are the primary source of studying interspecies variations, gene discovery, disease identification, and hypervariability in genome sequences. However, in vivo analysis of sequence data is costly and time-consuming. In addition, many next-generation sequencing tools have been developed to analyze sequence data having several new features and objectives. therefore, researchers always need a wide range of in silico microsatellite exploring tools to examine research data apart from extracting microsatellites. this study aims to identify additional features of simple sequence repeats, such as their location in cds, t- RNA, and rRNA regions. Also, to mark them as coding, the noncoding and coding-non-coding areas of upper bounds and lower bounds, the flanking sequences, statistics information of repeats, and other genomic features using pattern matching and computational geometry applications using range tree search algorithms. this study may help researchers retrieve and analyze the enhanced simple sequence repeats features and fill the gap for future studies and applications. © 2022 IEEE.

关键词： data mining

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A Physics and data-Driven Hybrid PINNs Intelligent computing Method for Nuclear Engineering Simulation 4

A Physics and Data-Driven Hybrid PINNs Intelligent Computing...

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4th international conference on Electronic Information Engineering and Computer science, EIECS 2024

作者： Xie, Yufei Wang, Wenlin Wu, Guohua Yu, Yang An, Ping Sun, Zibin Zhang, Haichuan Luo, Shengfeng Li, Yue School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Nuclear Power Institute of China Chengdu China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798331531409

In the field of nuclear energy, the Loss of Coolant Accident (LOCA) is recognized as one of the most severe types of nuclear reactor accidents, characterized by its complex physical processes and potentially catastrophic consequences. these challenges impose stringent requirements on safety analysis and emergency response. Accurate prediction and analysis of fluid behavior within pipelines under LOCA conditions are critical for evaluating accident outcomes and formulating response strategies. this paper introduces an innovative intelligent computing approach - a Physics-Informed Neural Networks (PINNs) model driven by both physical data and simulation data, specifically tailored for LOCA conditions. To address the challenges posed by multivariable and complex physical relationships, the six-equation two-fluid model is first simplified to represent the physical processes. Subsequently, a dual-driven PINNs network is developed, integrating both simulation data and physical constraints. the proposed model demonstrates a root mean square error (RMSE) of 0.02, a mean absolute error (MAE) of 0.044, and an R2 of 0.81 in predicting the outcomes under the six-equation model. © 2024 IEEE.

关键词： Nuclear energy

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